Purpose

• Goals of this talk are:

1. Expose you to a programming language (R)
2. Demonstrate why you might want to use R
3. Show you how to download and install…R
4. Display some R examples
5. Provide useful R resources

• What is not intended:

1. A step-by-step tutorial on how to use R
2. To convince everyone R is the language for them

About me

• Worked at ANL since 2013
• B.Sc. 2015 in Biology and Chemistry, Elmhurst College
• M.Sc. 2019 in Environment and Sustainability, University of Saskatchewan

• Only used R since 2017 for my Master’s research

What is R?

• “R is a language and environment for statistical computing and graphics”– R Project

  

• Basically, R is an open-source tool that provides users a way to analyze and visualize data in a reproducible way
• But can’t I do that in Excel? I mean, the equations I use are documented in each cell and I can totally make a killer XY scatter plot.

• 

The good and bad of Excel

What exactly does Reproducibility mean?

• “To produce a copy of something or to show or do something again”
• And as scientists, we strive for reproducible results
  • Experimental Design:
    • “Experiment was carried out x times with n number of samples”
  • Lab notebooks
  • Presentations
  • Publications
    

    https://www.nature.com/articles/s41562-016-0021

In data analysis, reproducibility usually means building a Reproducible environment

• This means:
  • Keeping track of how results were produced (a roadmap from raw data to conclusions)
  • Avoiding hidden data manipulation edits
  • Version control (e.g. Git) where versions can be saved
  • Clearly noting what you did and why you did it (i.e. “commenting out”, #)
  • Building a hierarchical analysis and check-points along the way
  • Sharing your work

• Reproducible work:
  • Enables efficient collaboration
  • Makes your future-self less prone to this

You can’t spell reproducible without R

• R excels at:

1. Handling large amounts of data
2. Automating analyses
3. Transparency (of analyses)
4. High-quality graphics
   1. Extremely customizable (size, resolution, filetype, etc.)
5. Version control (R makes any imported file an object and does not overwrite)
6. Handling diverse file types
7. Analysis Checkpoints (Allows users to put tests in place to identify errors)
8. Highly Reproducible
9. Creating Reports and Presentations (Like this one!)

Will I really ever need to learn R?

But who cares about big tech using R, how will it help me as an aspiring scientist?

Packages? What packages?

• R is a programming language that can use “packages” to accomplish nearly any task

• Analogy time:
  • Imagine R is a basic swiss army pocket knife
  • Packages are the can openers, cork screws, serrated blades, tweezers, etc.
  • You can accomplish nearly any task with a basic swiss army pocket knife (Base R), but that nail file is a game-changer

Where to download R

• https://cran.r-project.org/mirrors.html
  1. Choose location nearest you
  2. Download for your operating system
  3. Install R for the first time
  4. Download latest R version (Here, 3.6.1) for your operating system

• Downloading R Tutorial: https://medium.com/@GalarnykMichael/install-r-and-rstudio-on-windows-5f503f708027

Download RStudio (Integrated Development Environment)

• An IDE is a way to interact with R language without using the command line (Windows)/ terminal (macOS)
• https://www.rstudio.com/products/rstudio/download/#download
  1. Click “RStudio Desktop-Free-Download”
  2. Download for your OS in “Installers for Supported Platforms”

This is all great and I’m loving it, but what can R actually do?

• Some Capabilities of R:
  1. Creates reproducible Word, Powerpoint, PDF, and HTML Documents
  2. Build interactive web apps
  3. Use other programming languages with/within R
  4. Machine Learning
  5. Produce interactive plots
  6. Web-scrape data

• More: https://blog.revolutionanalytics.com/2012/07/a-big-list-of-the-things-r-can-do.html

R Example: Variable Assignment and Arithmetic

x <- 2 # read as "assign x to be a value of 2"
y <- 10 # read as "assign y to be a value of 10"
print(x + y) # read as "show me what x plus y equals"

## [1] 12

R Example: Importing Excel File

excel_data <- readxl::read_xlsx("excel_example.xlsx")
head(excel_data) # show the first 6 rows

## # A tibble: 6 x 7
##   `First Name` `Last Name` Gender Country         Age Date          Id
##   <chr>        <chr>       <chr>  <chr>         <dbl> <chr>      <dbl>
## 1 Dulce        Abril       Female United States    32 15/10/2017  1562
## 2 Mara         Hashimoto   Female Great Britain    25 16/08/2016  1582
## 3 Philip       Gent        Male   France           36 21/05/2015  2587
## 4 Kathleen     Hanner      Female United States    25 15/10/2017  3549
## 5 Nereida      Magwood     Female United States    58 16/08/2016  2468
## 6 Gaston       Brumm       Male   United States    24 21/05/2015  2554

R Example: Map of Study Sites from Thesis

-Thesis link: https://harvest.usask.ca/bitstream/handle/10388/11889/DYLLA-THESIS-2019.pdf?sequence=1&isAllowed=y

R Example: Interactive Population Map of USA

library(plotly)
df <- read.csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_us_cities.csv')

df$q <- with(df, cut(pop, quantile(pop)))
levels(df$q) <- paste(c("1st", "2nd", "3rd", "4th", "5th"), "Quantile")
df$q <- as.ordered(df$q)

g <- list(
  scope = 'usa',
  projection = list(type = 'albers usa'),
  showland = TRUE,
  landcolor = toRGB("gray85"),
  subunitwidth = 1,
  countrywidth = 1,
  subunitcolor = toRGB("white"),
  countrycolor = toRGB("white")
)

p <- plot_geo(df, locationmode = 'USA-states', sizes = c(1, 250)) %>%
  add_markers(
    x = ~lon, y = ~lat, size = ~pop, color = ~q, hoverinfo = "text",
    text = ~paste(df$name, "<br />", df$pop/1e6, " million")
  ) %>%
  layout(title = '2014 US city populations<br>(Click legend to toggle)', geo = g)
p

R Example: Interactive 3D Bubble Plot of Life Expectancy vs GDP vs Population

data <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/gapminderDataFiveYear.csv")

data_2007 <- data[which(data$year == 2007),]
data_2007 <- data_2007[order(data_2007$continent, data_2007$country),]
data_2007$size <- data_2007$pop
colors <- c('#4AC6B7', '#1972A4', '#965F8A', '#FF7070', '#C61951')

pl <- plot_ly(data_2007, x = ~gdpPercap, y = ~lifeExp, z = ~pop, color = ~continent, size = ~size, colors = colors,
             marker = list(symbol = 'circle', sizemode = 'diameter'), sizes = c(5, 150),
             text = ~paste('Country:', country, '<br>Life Expectancy:', lifeExp, '<br>GDP:', gdpPercap,
                           '<br>Pop.:', pop)) %>%
  layout(title = 'Life Expectancy v. Per Capita GDP, 2007',
         scene = list(xaxis = list(title = 'GDP per capita (2000 dollars)',
                      gridcolor = 'rgb(255, 255, 255)',
                      range = c(2.003297660701705, 5.191505530708712),
                      type = 'log',
                      zerolinewidth = 1,
                      ticklen = 5,
                      gridwidth = 2),
               yaxis = list(title = 'Life Expectancy (years)',
                      gridcolor = 'rgb(255, 255, 255)',
                      range = c(36.12621671352166, 91.72921793264332),
                      zerolinewidth = 1,
                      ticklen = 5,
                      gridwith = 2),
               zaxis = list(title = 'Population',
                            gridcolor = 'rgb(255, 255, 255)',
                            type = 'log',
                            zerolinewidth = 1,
                            ticklen = 5,
                            gridwith = 2)),
         paper_bgcolor = 'rgb(243, 243, 243)',
         plot_bgcolor = 'rgb(243, 243, 243)')
pl

Useful R Links

• Free Resources
  • RStudio Resources: https://www.rstudio.com/online-learning/#r-programming
  • UCLA: https://stats.idre.ucla.edu/
  • 2 minute R Tutorials: https://www.youtube.com/playlist?list=PLcgz5kNZFCkzSyBG3H-rUaPHoBXgijHfC
  • Short R Course: http://faculty.washington.edu/tlumley/Rcourse/
  • Statistics in R: http://zoonek2.free.fr/UNIX/48_R/all.html (ANL Blocked)
  • “R by Example”: http://www.mayin.org/ajayshah/KB/R/index.html

• Blogs (Actually super useful)
  • R Bloggers: https://www.r-bloggers.com/
  • ComputerWorld: https://www.computerworld.com/article/2497143/business-intelligence-beginner-s-guide-to-r-introduction.html
  • Interesting Topics: https://blog.revolutionanalytics.com/
  • Excel vs. R: https://www.jessesadler.com/post/excel-vs-r/

• Communities
  • StackOverflow: https://stackoverflow.com/questions/tagged/r
  • Twitter: https://twitter.com/search?q=%23rstats

• Books
  • Free
    • “R for Data Science”: https://r4ds.had.co.nz/
    • “An Introduction to R”: https://cran.r-project.org/doc/manuals/R-intro.pdf
  • Not Free
    • “R Cookbook”:https://www.amazon.com/Cookbook-Analysis-Statistics-Graphics-Cookbooks/dp/0596809158/ref=as_li_ss_tl?s=books&ie=UTF8&qid=1515977919&sr=1-4&keywords=r+cookbook&linkCode=sl1&tag=nandeshwarinf-20&linkId=102d1e38b5fb3b390f72d55ed1ce476d
    • The Art of R Programming; https://www.amazon.com/Art-Programming-Statistical-Software-Design/dp/1593273843/ref=as_li_ss_tl?s=books&ie=UTF8&qid=1515977954&sr=1-1&keywords=The+Art+of+R+Programming&linkCode=sl1&tag=nandeshwarinf-20&linkId=8d812de94763fc2f8a551d6571a69108
    • “R for Excel Users”: https://www.amazon.com/Excel-Users-Introduction-Analysts-ebook/dp/B01K3HFOZU/ref=as_li_ss_tl?s=books&ie=UTF8&qid=1515978598&sr=1-2&keywords=r+for+excel+users&linkCode=sl1&tag=nandeshwarinf-20&linkId=956c11fdd540d039e7b8c285459f2517

• Online Courses
  • https://www.quora.com/What-is-the-best-online-course-to-learn-R